FDA De Novo Classification Strategy

by Dr. Ebot Eyong, PhD, CQE, RAC.

Executive Summary

Medical innovation often advances faster than regulatory precedent. While the FDA’s 510(k) pathway provides an efficient route to market for devices that can demonstrate substantial equivalence to an existing legally marketed product, truly novel technologies frequently face a different challenge: there is no appropriate predicate device.

This was the situation faced by a rapidly growing digital health company that had developed an artificial intelligence–enabled clinical decision support platform designed to assist healthcare professionals in interpreting complex physiological data. Technology incorporated machine learning algorithms, cloud-based analytics, and an intuitive user interface to support clinical workflows. Although early evaluations demonstrated promising performance and strong clinician acceptance, the regulatory pathway remained uncertain.

The company’s leadership initially hoped to pursue a Traditional 510(k). However, after preliminary regulatory research, it became apparent that no legally marketed predicate device possessed the same intended use and technological characteristics. Attempting to force the technology into an unsuitable 510(k) framework risked a Not Substantially Equivalent (NSE) determination, unnecessary delays, and increased development costs.

Recognizing the need for an experienced regulatory partner, the company engaged E&E Medicals to evaluate available FDA pathways, develop a comprehensive De Novo strategy, coordinate interactions with FDA reviewers, and establish the scientific foundation necessary to support the first marketing authorization for this new category of medical technology.

Company Background

The client was an innovative healthcare technology company founded by physicians, biomedical engineers, software architects, and data scientists. Its mission was to leverage artificial intelligence to improve the efficiency and consistency of clinical decision-making while preserving physician oversight.

The platform integrated multiple sources of patient information — including physiological measurements, laboratory data, and historical trends — to generate structured analyses intended to support healthcare professionals during routine clinical evaluations. Importantly, the system was designed to assist clinicians rather than replace medical judgment, with all recommendations subject to independent professional review.

The company’s technology represented years of investment in algorithm development, software engineering, cybersecurity, human-centered design, and clinical collaboration. Venture capital funding had accelerated product development, and investor expectations centered on achieving U.S. commercialization within an aggressive timeline.

Despite the technical sophistication of the product, leadership understood that regulatory success would ultimately determine whether the technology could reach healthcare providers and patients.